Hellinger Optimal Criterion and 𝓗𝑷𝑨- Optimum Designs for Model Discrimination and Probability-Based Optimality
Journal Title: International Journal of Mathematics and Statistics Invention - Year 2017, Vol 5, Issue 2
Abstract
Kullback-Leibler (KL) optimality criterion has been considered in the literature for model discrimination. However, Hellinger distance has many advantages rather than KL-distance. For that reason, in this paper a new criterion based on the Hellinger distance named by Hellinger (ℋ) -optimality criterion is proposed to discriminate between two rival models. An equivalence theorem is proved for this criterion. Furthermore, a new compound criterion is constructed that possess both discrimination and a high probability of desired outcome properties. Discrimination between binary and Logistic GLM are suggested based on the new criteria.
Authors and Affiliations
W. A. Hassanein, N. M. Kilany
Keywords
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